1. Field of the Invention
The invention relates to method and apparatus for population segmentation. In particular, the invention relates to a method and system of household-level segmentation.
2. Related Art
For marketing purposes, knowledge of customer behavior is important, if not crucial. For direct marketing, for example, it is desirable to focus the marketing on a portion of the segment likely to purchase the marketed product or service.
In this regard, several methods have traditionally been used to divide the customer population into segments. The goal of such segmentation methods is to predict consumer behavior and classify consumers into clusters based on observable characteristics. Factors used to segment the population into clusters include demographic data such as age, marital status, and income and behavioral data such as tendency to purchase a particular product or service.
In dividing the population into segments, it is desired to maximize the homogeneity within a cluster, while maximizing the distinctness across clusters. In this regard, traditional segmentation schema have employed a two-stage process involving targeted optimization and cluster evaluation. These schema can begin either with behavior (behaviorally driven) or with demographics (demographically driven).
FIG. 1 illustrates a traditional, behaviorally driven segmentation process 100. At block 110, a set of clusters of households is defined based on common behaviors within each cluster. The clusters are defined such that the behaviors within each cluster are as similar as possible, while being as different as possible across clusters. At block 120, the clusters are evaluated for demographics to determine whether the demographics of each cluster are sufficiently similar within the cluster, while being sufficiently different across the clusters. At block 130, if the demographics do not satisfy the criteria, the process is repeated from block 110 until an optimal segmentation is achieved. Although this iterative method may result in a useful segmentation system, it fails to directly provide a solution that defines clusters based on demographics.
FIG. 2 illustrates a traditional, demographically driven segmentation process 200. At block 210, a set of clusters of households is defined based on common demographics within each cluster. The clusters are defined such that the demographics within each cluster are as similar as possible, while being as different as possible across clusters. At block 220, the clusters are evaluated for behaviors to determine whether the behaviors of each cluster are sufficiently similar within the cluster, while being sufficiently different across the clusters. At block 230, if the behaviors do not satisfy the criteria, the process is repeated from block 210 until an optimal segmentation is achieved. Similarly to the system described above with reference to FIG. 1, the system of FIG. 2 fails to directly provide a solution that defines clusters based on behavior.
Thus, while these traditional, iterative methods may result in a useful segmentation system, they fail to directly provide a solution that defines clusters based jointly on behavior and demographics.